What characterizes a normal distribution?

A normal distribution is specifically characterized by a symmetrical bell-shaped curve where most of the data points cluster around the mean. This shape indicates that scores near the mean are more frequent than those further away, leading to a decrease in frequency as you move toward the extremes.

In this context, the phrase "most scores fall near the mean" highlights the fundamental property of the distribution, demonstrating that the mean, median, and mode are all located at the center. The symmetry of the curve means that the distribution of scores on either side of the mean is balanced, further reinforcing the characteristics of normality.

Other options mention aspects that do not accurately describe the nature of a normal distribution. For instance, while it is true that scores may be evenly distributed in certain contexts, a key characteristic of a normal distribution is not merely even distribution but rather the specific clustering of scores around the mean that creates the bell curve. The concentration of highest scores in the middle does occur in a normal distribution, but formulating it this way doesn’t capture the entirety of the distribution's structure. Lastly, stating that scores have no regular pattern completely disregards the consistent and predictable shape that characterizes a normal distribution.